Identification of the gut microbiome compositions associated with disease has become a research focus worldwide. Emerging evidence has revealed the presence of gut microbiota dysbiosis in Parkinson's disease. In this study, we aimed to identify the gut microbiome associated with Parkinson's disease and subsequently to screen and to validate potential diagnostic biomarkers of Parkinson's disease. This case-control study investigated gut microbial genes in faeces from 40 volunteer Chinese patients with Parkinson's disease and their healthy spouses using shotgun metagenomic sequencing. Furthermore, the identified specific gut microbial gene markers were validated with real-time PCR in an independent Chinese cohort of 78 Parkinson's disease patients, 75 control subjects, 40 patients with multiple system atrophy and 25 patients with Alzheimer's disease. We developed the first gut microbial gene catalogue associated with Parkinson's disease. Twenty-five gene markers were identified that distinguished Parkinson's disease patients from healthy control subjects, achieving an area under the receiver operating characteristic curve (AUC) of 0.896 (95% confidence interval: 83.1-96.1%). A highly accurate Parkinson's disease index, which was not influenced by disease severity or Parkinson's disease medications, was created. Testing these gene markers using quantitative PCR distinguished Parkinson's disease patients from healthy controls not only in the 40 couples (AUC = 0.922, 95% confidence interval: 86.4-98.0%), but also in an independent group of 78 patients with Parkinson's disease and 75 healthy control subjects (AUC = 0.905, 95% confidence interval: 86.0-95.1%). This classifier also performed a differential diagnosis power in discriminating these 78 patients with Parkinson's disease from a cohort of 40 patients with multiple system atrophy and 25 patients with Alzheimer's disease based on the panel of 25 biomarkers. Based on our results, the identified Parkinson's disease index based on the gene set from the gut microbiome may be a potential diagnostic biomarker of Parkinson's disease.